Relating in-situ soil moisture data to remotely sensed spectral profiles

Soil moisture is a significant but often poorly constrained component of the hydrologic cycle. Gridded soil moisture products are very useful for quantifying spatial variability and constraining hydrological models. Soil moisture measurements from in-situ sensors represent values at a small spatial scale and cannot fully represent watershed-scale variability. Current satellite platforms collecting data in the primary bands used for soil moisture provide coarser gridded data than desired for many hydrologic assessment and modeling applications. Other frequency bands that are less optimal for soil moisture retrieval have also been shown to have some dependence on soil moisture and are readily available at higher spatial resolution. In this work, we relate in-situ soil moisture data to these higher resolution, sub-optimal frequency bands through a simple model connecting soil moisture to spectral reflectance profiles across frequencies from multiple satellites. We present preliminary results and show how these methods can be used to improve the resolution of gridded remotely sensed soil moisture data sets.